Manual monitoring breaks down
Traditional security relies on people watching screens. Attention drops over long shifts — miss rates run as high as 40% — and the model just doesn't scale to thousands of camera feeds.
Delivered / Production
GSAI · 2026 · 05 · 0017
AI Security SolutionSmart security recognition, from passive monitoring to active intelligent defense
Next-gen AI security built on edge computing and multimodal large models — face recognition, behavior analysis, vehicle ID, and event alerts, end to end. 24/7 active monitoring with sub-second response and 99.7% accuracy.
6 min read · v2.1 · updated May 2026
99.7%
Detection accuracy
30-day observation window
<50ms
Real-time latency
Edge inference
10M+
Identified events
120+ customer scenarios
24/7
Always on
99.95% uptime
Live recognition panel
Channel
CH-08 / Lobby South
Inference latency
< 50 ms
Detection accuracy
99.7%
Event
Loitering · Reported
Detection accuracy
99.7%
Edge · 24/7
01The Problem
Where traditional security falls short
As cities scale and security demands grow, traditional systems hit clear ceilings on monitoring efficiency, response time, and false-alert control. Enterprises and public spaces need a system that actively identifies risk, alerts in real time, and coordinates across subsystems.
Forensic review is painful
Without smart indexing, post-event review means watching footage frame-by-frame — 4–8 hours on average. Response and resolution slow to a crawl.
False alerts dominate
Traditional motion detection trips on lighting, weather, and animals. False-alert rates over 60% leave security teams chasing nothing while real threats slip through.
Subsystems can't coordinate
Access control, video, and alarms run as silos. No shared data, no joint response — and no unified command when an incident actually happens.
02The Approach
Three technical paths, systematically evaluated
We don't commit blindly to a single tech stack. After systematically evaluating cloud APIs, edge computing, and large models, we landed on a hybrid architecture — edge first, cloud-coordinated, model-augmented — striking the right balance of latency, accuracy, and cost.
AAlibaba Cloud / Huawei Cloud Vision APIs
Cloud AI analytics
Strengths
- Fast deploy, out-of-the-box
- Algorithms updated continuously
- Elastic scale with no ceiling
Limitations
- Network latency hurts real-time use
- Cloud data raises compliance risk
- Long-term cost grows with volume
94.2%
Accuracy
200ms
Latency
¥0.03
Cost per call
Good for non-real-time supporting analysis
BNVIDIA Jetson / HiSilicon chips
Edge computing + local inference
Strengths
- Ultra-low latency (<50ms)
- Data stays local, strong compliance
- Offline-capable, high reliability
Limitations
- Compute limited by hardware
- Model updates require on-site deployment
- Higher upfront hardware investment
97.8%
Accuracy
35ms
Latency
¥0.005
Cost per call
Core real-time recognition engine
CIn-house + open-source fine-tuning
Multimodal large-model fusion
Strengths
- Understands complex scene semantics
- Natural-language query support
- Self-learning, self-optimizing
Limitations
- High compute demand at inference
- Long training cycles
- Requires high-quality labeled data
99.1%
Accuracy
800ms
Latency
¥0.08
Cost per call
Complex behavior analysis and decision support
Hybrid decision
Edge-first · Cloud-coordinated · Model-augmented
Final architecture uses edge computing (Path B) as the core real-time inference engine — guaranteeing <50ms response and keeping data local. The cloud platform (Path A) handles model training, large-scale retrieval, and cross-region coordination. Multimodal large models (Path C) handle complex behavior semantics and decision support. The three layers coordinate through a unified model management platform, hitting 99.7% combined accuracy.
03How It Works
Modular, explainable, gracefully degradable
The system runs as a five-stage pipeline. Each stage is independently testable and swappable. When any stage fails, the system gracefully degrades to a rule-engine fallback — keeping the lights on 24/7. Every inference comes with a confidence score for human review and continuous model improvement.
Capture and preprocessing
Multi-stream video ingest with automatic enhancement, denoising, and frame-rate normalization for clean input.
RTSP/ONVIFH.265 decodingFrame enhancement
Detection and tracking
Multi-object real-time detection on an improved YOLOv8 model, with DeepSORT for cross-frame tracking and ReID.
YOLOv8DeepSORTReID
Feature extraction
Parallel computation of face feature vectors, behavior skeleton keypoints, and license plate OCR.
ArcFaceHRNetCRNN
Semantic analysis and decision
Multimodal feature fusion combined with scene context — for behavior understanding that triggers preset rules or AI decisions.
TransformerRule engineKnowledge graph
Alert, coordination, closure
Real-time push of anomaly events with coordinated triggers across access control, broadcast, and lighting — closing the detect-alert-respond-archive loop.
MQTTWebSocketCoordination
System layers
L1
Access layerVideo stream ingest, device management, protocol adaptation
L2
Inference layerEdge inference, model loading, feature compute
L3
Business layerRule engine, event management, coordinated dispatch
L4
Data layerFeature store, event store, model registry, logs
04Delivery
End-to-end project management, diagnosis to delivery
We run an agile, iterative process across 7 key phases. Each phase has clearly defined deliverables and acceptance criteria — keeping the project on time and on quality.
Week 0
Scenario diagnosis
On-site assessment to map the existing security architecture, device inventory, and business workflow — and pin down the core pain points and priorities.
On-site assessment reportDevice compatibility reviewRequirements priority matrix
Week 1–2
Solution design
Design the system architecture based on the diagnosis: tech selection, hardware configuration, deployment plan — captured in a detailed solution document.
Solution doc v1.0Hardware selection listNetwork topology design
Week 3–5
Model training and adaptation
Collect customer-scenario data, fine-tune the model, validate metrics in a test environment.
Scenario datasetFine-tuned model packageTest report
Week 6–7
System integration and deployment
Deploy edge devices, integrate with the existing security platform, and verify cross-subsystem coordination.
Deployment playbookIntegration test reportOperations manual
Week 8–9
Pilot and tuning
Run the system in production trial. Monitor metrics continuously and tune parameters and rules based on real data.
Monitoring dashboardTuning logUAT test report
Week 10
Formal handover
Pass all acceptance tests, hand off operations docs and training materials, and formally transfer the system to the customer's ops team.
Acceptance reportTraining materialsOperations SLA
Ongoing
Operations and optimization
Continuous model updates, performance optimization, and technical support — for long-term stable performance.
Monthly ops reportModel update packageOptimization recommendations
05Outcomes
Quantifiable business gains
After 30 days of stable production, all key metrics hit or beat their targets. The numbers below come from real production environments, confirmed by the customer.
| Metric | Before | After | Improvement |
|---|---|---|---|
| Detection accuracy | 68.4% | 99.7% | +31.3pp |
| Response latency | 2.5s | <50ms | -98% |
| False-alert rate | 62% | 3.2% | -95% |
| Labor cost | 24 staff/shift | 6 staff/shift | -75% |
| Forensic review time | 4-8h | <5min | -99% |
| System uptime | 95.2% | 99.95% | +4.75pp |
After the system went live, response time dropped from minutes to seconds. The big surprise was the false-alert rate — the security team can finally focus on real threats. The whole project took 10 weeks from requirements to delivery. The engineering professionalism and execution were impressive.
Project sign-off milestones
Solution review approved2025.03.15 ✓
UAT passed2025.05.20 ✓
Formal handover2025.05.28 ✓
Operations stability confirmed2025.06.28 ✓
06Use Cases
Cross-industry AI security solutions
Built on a modular architecture, the system adapts quickly to security needs across industries and scenarios. The use cases below are validated deployments — each one can be configured to your specific requirements.
Public safety monitoring
Face deployment, crowd density tracking, and anomaly alerts for city public spaces, transit hubs, and large venues — supporting safer cities.
Smart traffic management
License plate recognition, traffic violation detection, road condition analysis, and intelligent signal control — moving traffic faster and reducing accidents.
Smart community management
Face-based access at gates, visitor management, object-throwing detection, and fire-lane obstruction monitoring — for safer, more convenient living.
Industrial safety
PPE detection, hazardous-zone intrusion alerts, equipment anomaly monitoring, and procedural compliance checks — keeping operations safe.
Smart campus security
Campus access control, stranger alerts, student behavior analysis, and perimeter monitoring — creating a safer learning environment.
Smart retail loss prevention
Foot traffic counting, heatmap analysis, anomaly detection, and VIP recognition — better shopping experience with lower shrinkage.
07Our Capabilities
Full-stack delivery, from algorithm to production
Wavesteam has spent years deep in AI vision. We cover the full chain — algorithm research, model training, system integration, and ongoing optimization. We aren't just a tech vendor; we're a long-term partner for the 0-to-1 of intelligent transformation.
Custom model training
Fine-tune and optimize models on your scenario data for peak recognition performance in your specific environment. The team has trained models across 50+ industry scenarios.
Edge deployment
Supports major edge platforms (NVIDIA Jetson, HiSilicon, Rockchip, and more). We handle model lightweighting and inference acceleration to hit real-time targets.
System integration
Full-cycle capability from solution design through deployment. We integrate cleanly with your existing security platform and business systems — reducing integration risk.
Operations and optimization
24/7 technical support and regular model updates. Online learning continuously improves system performance for long-term stable operation.
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Let's Talk
If you have a concrete workflow AI hasn't solved yet, let's figure out the right approach together.
We unpack the workflow with you, judge whether AI is worth using and which approach makes the most sense, then come back within 5 business days with a practical initial plan and estimate.
Business email
contact@boilingwater.cnOffice
10F, South Tower, Kingkey Yujing Times, Longgang District, Shenzhen